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- import numpy as np
- from tods.sk_interface.detection_algorithm.LSTMODetector_skinterface import LSTMODetectorSKI
- from sklearn.metrics import precision_recall_curve
- from sklearn.metrics import accuracy_score
- from sklearn.metrics import confusion_matrix
- from sklearn.metrics import classification_report
- import matplotlib.pyplot as plt
- from sklearn import metrics
-
- #prepare the data
- data = np.loadtxt("./500_UCR_Anomaly_robotDOG1_10000_19280_19360.txt")
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- X_train = np.expand_dims(data[:10000], axis=1)
- X_test = np.expand_dims(data[10000:], axis=1)
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- transformer = LSTMODetectorSKI()
- transformer.fit(X_train)
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- prediction_labels_train = transformer.predict(X_train)
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- prediction_labels = transformer.predict(X_test)
- prediction_score = transformer.predict_score(X_test)
-
- print("Prediction Labels\n", prediction_labels)
- print("Prediction Score\n", prediction_score)
-
- # y_true = prediction_labels_train[:1000]
- # y_pred = prediction_labels[:1000]
- y_true = prediction_labels_train
- y_pred = prediction_labels
-
- print('Accuracy Score: ', accuracy_score(y_true, y_pred))
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- confusion_matrix(y_true, y_pred)
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- print(classification_report(y_true, y_pred))
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- precision, recall, thresholds = precision_recall_curve(y_true, y_pred)
- f1_scores = 2*recall*precision/(recall+precision)
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- print('Best threshold: ', thresholds[np.argmax(f1_scores)])
- print('Best F1-Score: ', np.max(f1_scores))
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- fpr, tpr, threshold = metrics.roc_curve(y_true, y_pred)
- roc_auc = metrics.auc(fpr, tpr)
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- plt.title('ROC')
- plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
- plt.legend(loc = 'lower right')
- plt.ylabel('True Positive Rate')
- plt.xlabel('False Positive Rate')
- plt.show()
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